size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9968744
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix" "array"
head( m )
x1 x2 x3
[1,] 0.2929961 0.4210332 0.3173651
[2,] -1.6990798 -1.4316851 -1.6082552
[3,] 1.0542408 1.0420483 0.9950902
[4,] 1.3992279 1.3768847 1.3432113
[5,] 0.6802669 0.5314773 0.5923502
[6,] -1.5372963 -1.6692010 -1.4788065
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.984 0.115 0.977 0.117 0.112 0.969
y3 0.984 1.000 0.050 0.978 0.047 0.038 0.984
x2 0.115 0.050 1.000 0.042 0.993 0.990 0.165
y1 0.977 0.978 0.042 1.000 0.056 0.048 0.970
x1 0.117 0.047 0.993 0.056 1.000 0.996 0.164
x3 0.112 0.038 0.990 0.048 0.996 1.000 0.155
y2 0.969 0.984 0.165 0.970 0.164 0.155 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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